JoinQuant API Help

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### Initializing Context with Query

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Example of initializing a context object with a query. Note that variables starting with '__' in 'g' are ignored during state saving.

```python
def initialize(context):
    g.query = query(valuation)
```

--------------------------------

### Multiple Stock Holding Example

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

This example demonstrates a strategy for managing multiple stocks. It buys 100 shares if the price is 0.5% above the 3-day average and sells if it's 0.5% below. Ensure 'use_real_price' is enabled for accurate results.

```python
# Import JoinQuant function library
import jqdata

def initialize(context):
    # Initialize this strategy
    # Set the stock pool we want to operate on
    g.stocks = ['000001.XSHE','000002.XSHE','000004.XSHE','000005.XSHE']
    # Set CSI 300 as the benchmark
    set_benchmark('000300.XSHG')
    # Enable dynamic复权 mode (real price)
    set_option('use_real_price', True)

# Function called at each time unit (daily for daily backtesting, per minute for minute backtesting)
def handle_data(context, data):
    # Loop through each stock
    for security in g.stocks:
        # Get the 3-day average price of the stock
        vwap = data[security].vwap(3)
        # Get the closing price of the stock at the previous time point
        price = data[security].close
        
        # If the price is 0.5% higher than the 3-day average, buy 100 shares
        if price > 1.005*vwap:
            order(security, 100)
            log.info("Buying %s" % (security))
        # If the price is 0.5% lower than the 3-day average, sell
        elif price < 0.995*vwap:
            order_target(security, 0)
            log.info("Selling %s" % (security))
```

--------------------------------

### Get Order Information

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves historical and current order information.

```python
get_orders(security)
```

--------------------------------

### Get Backtest and Simulation Information

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves information about backtests and simulations from the research environment.

```python
get_backtest()
```

--------------------------------

### Create Backtest from Strategy ID

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Creates a backtest by referencing a strategy ID from the research environment. This allows for programmatic backtest setup.

```python
create_backtest(strategy_id)
```

--------------------------------

### Get Concept List

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves a list of all available concept classifications.

```python
get_concepts()
```

--------------------------------

### Get All Securities Information

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves information for all available securities (stocks, futures, etc.) on the platform.

```python
get_all_securities(types, date)
```

--------------------------------

### Get Macroeconomic Data

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves macroeconomic data from JoinQuant's database using macro.run_query.

```python
macro.run_query(query_id)
```

--------------------------------

### Get All Trading Days

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves a list of all valid trading days within the JoinQuant system.

```python
get_all_trade_days()
```

--------------------------------

### Get All Factor Information

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves information about all factors available in the JoinQuant factor library. This is useful for exploring available quantitative factors.

```python
get_all_factors()
```

--------------------------------

### Get Industry List

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves a list of all available industry classifications.

```python
get_industries()
```

--------------------------------

### Setting Benchmark for Backtesting

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Specify a benchmark for comparing strategy performance during backtesting. The benchmark's starting point is the closing price of the trading day before the backtest begins.

```python
set_benchmark('000300.XSHG')
```

--------------------------------

### Get Open Orders

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves a list of all currently open (unfulfilled) orders.

```python
get_open_orders(security)
```

--------------------------------

### Get Single Security Information

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves detailed information for a specific security.

```python
get_security_info(security)
```

--------------------------------

### Get Margin Cash Stocks List

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves a list of securities eligible for margin cash borrowing.

```python
get_margincash_stocks()
```

--------------------------------

### Morning Sell All Stocks

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Executes at the start of each trading day to sell all currently held stocks.

```python
def morning_sell_all(context):
    for security in context.portfolio.positions:
        order_target(security, 0)
        log.info("Selling %s" % (security))
```

--------------------------------

### Trading Strategy Example

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

This Python function demonstrates a basic trading strategy that buys a security if its current price is 1% above the 5-day moving average and sells if it drops below the 5-day moving average. It logs buy/sell actions and records the stock price.

```python
def market_open(context):
security = g.security
# 获取股票的收盘价
close_data = attribute_history(security, 5, '1d', ['close'])
# 取得过去五天的平均价格
MA5 = close_data['close'].mean()
# 取得上一时间点价格
current_price = close_data['close'][-1]
# 取得当前的现金
cash = context.portfolio.available_cash
# 如果上一时间点价格高出五天平均价1%, 则全仓买入
if current_price > 1.01*MA5:
# 用所有 cash 买入股票
order_value(security, cash)
# 记录这次买入
log.info("Buying %s" % (security))
# 如果上一时间点价格低于五天平均价, 则空仓卖出
elif current_price < MA5 and context.portfolio.positions[security].closeable_amount > 0:
# 卖出所有股票,使这只股票的最终持有量为0
order_target(security, 0)
# 记录这次卖出
log.info("Selling %s" % (security))
# 画出上一时间点价格
record(stock_price=current_price)
```

--------------------------------

### Get Margin Security Stocks List

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves a list of securities eligible for margin security lending (short selling).

```python
get_marginsec_stocks()
```

--------------------------------

### Get Stock Concept Holdings

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Queries which concept sectors a stock belongs to.

```python
get_concept(security)
```

--------------------------------

### Get Future Contracts List

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves a list of all tradable future contracts for a given instrument.

```python
get_future_contracts(symbol)
```

--------------------------------

### Get Current Available Cash

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves the current available cash balance from the portfolio.

```python
cash = context.portfolio.available_cash
```

--------------------------------

### Get Valuation Data

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves market capitalization data for multiple securities within a specified date range. Useful for valuation analysis.

```python
get_valuation(securities, start_date, end_date)
```

--------------------------------

### Get Margin Trading Information

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves comprehensive margin trading information, including available stocks, rates, and positions.

```python
get_mtss()
```

--------------------------------

### Get Industry Constituent Stocks

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves a list of stocks belonging to a specific industry.

```python
get_industry_stocks(industry_name)
```

--------------------------------

### Get Concept Constituent Stocks

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves a list of stocks associated with a specific concept or theme.

```python
get_concept_stocks(concept_name)
```

--------------------------------

### Get Index Constituent Stocks

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves a list of stocks that are part of a specified index.

```python
get_index_stocks(index_symbol)
```

--------------------------------

### Get Trading Days in a Range

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves a list of trading days within a specified date range.

```python
get_trade_days(start_date, end_date)
```

--------------------------------

### Get Trade Information

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves information about executed trades, which are the actual transactions resulting from orders.

```python
get_trades(security)
```

--------------------------------

### Get Money Flow Information

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves information about the money flow of securities, which can indicate buying or selling pressure.

```python
get_money_flow(securities, start_date, end_date)
```

--------------------------------

### Get Index Constituent Weights

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves the weights of constituent stocks within a specified index.

```python
get_index_weights(index_symbol, date)
```

--------------------------------

### Get Factor Values

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves factor data, including quality, fundamental, sentiment, growth, risk, and per-share metrics. Supports hundreds of factors.

```python
get_factor_values(securities, start_date, end_date, factors)
```

--------------------------------

### Get Trading Day for a Specific Time

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Determines the trading day corresponding to a specific timestamp for a given security.

```python
get_trade_day(security, timestamp)
```

--------------------------------

### Get Current Time Data

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Fetches the current data for a given security at the current time. This is useful for strategies that need up-to-the-minute information.

```python
get_current_data()
```

--------------------------------

### Get Historical Data with get_price

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves historical data for multiple securities and multiple data fields. Returns data in a DataFrame format.

```python
get_price(securities, start_date, end_date, fields)
```

--------------------------------

### Get Factor Kanban Values

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves data from the factor kanban list. This function is useful for accessing pre-defined factor groupings or analyses.

```python
get_factor_kanban_values(kanban_id)
```

--------------------------------

### Get Historical Fundamental Data for Multiple Periods

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves historical fundamental data for multiple quarters or years. Useful for long-term fundamental analysis.

```python
get_history_fundamentals(securities, start_date, end_date, fields)
```

--------------------------------

### Get Historical Data with history

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves historical data for multiple securities for a single data field. Returns data as a DataFrame or Dict.

```python
history(securities, start_date, end_date, field)
```

--------------------------------

### Get Fund Net Asset Value and Futures Settlement Price

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves additional data such as fund unit/cumulative net asset value and futures settlement price/open interest.

```python
get_extras(securities, start_date, end_date, fields)
```

--------------------------------

### Portfolio Optimization Models

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Demonstrates setting up different portfolio optimization models with various constraints and objectives. This includes equal weighting, risk parity, minimum variance, and maximizing Sharpe ratio or factor values.

```python
# Model 1: Equal weight allocation

# Model 2: Risk parity; total stock weight limited to 0-90%, total ETF weight limited to 0-10%; individual asset weight not exceeding 10%

# Model 3: Minimize portfolio risk (minimize portfolio variance); total portfolio weight limited to 90%-100%; target annualized return lower bound of 10%

# Model 4: Maximize 'Popularity Indicator 5-day Moving Average'; target annualized return lower bound of 10%; individual asset weight not exceeding 20%

# Model 5: Maximize portfolio Sharpe ratio; individual asset weight not exceeding 10%
```

--------------------------------

### Get Tick Data for Various Instruments

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves tick data for stocks, futures, options, and indices. This function provides granular market data.

```python
get_ticks(securities, start_time, end_time)
```

--------------------------------

### Get Latest Tick Data

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Fetches the most recent tick data for a security. This is useful for high-frequency trading or analyzing real-time market movements.

```python
get_current_tick(security)
```

--------------------------------

### Initialize Trading Strategy

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Sets up the trading environment, including enabling real-time pricing, defining the daily buy count, and selecting the stock universe.

```python
import jqdata

def initialize(context):
    set_option('use_real_price', True)
    g.daily_buy_count = 5
    g.stocks = get_industry_stocks('I64') + get_industry_stocks('I65')
    g.stocks = set(g.stocks)
    run_daily(morning_sell_all, '09:30')
```

--------------------------------

### Get Historical Data with get_bars

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves historical data, including snapshot data, for single or multiple securities and multiple data fields. Returns data as numpy.ndarray or DataFrame.

```python
get_bars(securities, count, fields, dt_format, freq)
```

--------------------------------

### Initialize Trading Environment

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Sets up the trading environment by defining the benchmark, enabling real-time pricing, and configuring order costs for stock trades. It also initializes an optimizer model and schedules monthly trading functions.

```python
import pandas as pd 
from jqdata import * 
from jqfactor import Factor 
from jqlib.optimizer import * 

def initialize(context):
    set_benchmark('000300.XSHG')
    set_option('use_real_price', True)
    set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, 
    min_commission=5), type='stock')
    g.optimizer = 2 
    optimize_model = {
    1:"模型1:等权重配置", 
    2:"模型2:组合风险平价;股票的总权重限制为0到90%,ETF的总权重限制为0到10%;每只标的权重不超过10%", 
    3:"模型3:组合风险最小化(最小化组合方差);组合总权重限制为90%到100%;组合年化收益率目标下限为10%", 
    4:"模型4:'人气指标5日均值'最大化;组合年化收益率目标下限为10%;每只标的权重不超过20%", 
    5:"模型5:组合夏普比率最大化;每只标的权重不超过10%"
    }
    print("优化%s"%(optimize_model[g.optimizer])) 
    run_monthly(before_market_open, monthday=1, time='9:00', reference_security='000300.XSHG')
    run_monthly(market_open, monthday=1, time='9:30', reference_security='000300.XSHG')
```

--------------------------------

### Get Historical Data with attribute_history

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves historical data for a single security across multiple data fields. Returns data as a DataFrame or Dict.

```python
attribute_history(security, start_date, end_date, fields)
```

--------------------------------

### Set Up Sub-Portfolios

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Initializes sub-portfolios for a strategy, allowing for segmented management of assets or strategies.

```python
set_subportfolios(sub_portfolios)
```

--------------------------------

### Get Dragon Tiger List Data

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves data from the Dragon Tiger List (Longhu Bang), which shows daily trading activity of major investors.

```python
get_billboard_list(date)
```

--------------------------------

### Get Call Auction Tick Data

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves tick data during the call auction period for a specified time interval. Useful for analyzing pre-market and post-market trading.

```python
get_call_auction(securities, start_time, end_time)
```

--------------------------------

### Initialize Backtest with Stock Tick Data

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Sets up a backtest for stock tick data, including benchmark, order costs, and subscription for tick events.

```python
# 导入函数库 
import jqdata 
def initialize(context): 
set_benchmark('000300.XSHG') 
set_option('use_real_price', True) 
set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock') 
def before_trading_start(context): 
subscribe('000001.XSHE','tick') 
def handle_tick(context, tick): 
log.info(tick) 
def after_trading_end(context): 
unsubscribe_all()
```

--------------------------------

### Initialize Margin Trading Account

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Configures a single margin trading account for futures and options trading.

```python
# 初始化融资融券账户 
# 初始化的仓位是不允许直接进行融资融券操作的,因为初始默认 subportfolios[0] 中 SubPortfolioConfig 的 type = 'stock',只允许买卖股票与场内基金等。 
# 因此要进行融资融券,您需要设定 SubPortfolioConfig 的 type = 'stock_margin',具体方法如下: 
def initialize(context): 
## 设置单个账户 
# 获取初始资金 
init_cash = context.portfolio.starting_cash 
# 设定账户为融资融券账户,初始资金为 init_cash 变量代表的数值(如不使用设置多账户,默认只有subportfolios[0]一个账户,Portfolio 指向该账户。) 
set_subportfolios([SubPortfolioConfig(cash=init_cash, type='stock_margin')])
```

--------------------------------

### Simple Complete Strategy

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

A basic strategy demonstrating initialization and daily order execution. It buys 1000 shares when no position exists and sells 800 shares when a position does.

```python
def initialize(context):
    # Define a global variable to store the stock to operate on
    g.security = '000001.XSHE'
    # Run function
    run_daily(market_open, time='every_bar')

def market_open(context):
    if g.security not in context.portfolio.positions:
        order(g.security, 1000)
    else:
        order(g.security, -800)
```

--------------------------------

### Initialize Backtest with Tick Frequency

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Initializes a backtest environment for tick data, setting up futures trading and daily scheduled functions.

```python
# 回测时间段2019-06-03到2019-06-04,运行频率设置为tick 
# 初始化 
def initialize(context): 
# 获取起始资金 
init_cash = context.portfolio.starting_cash 
# 交易品种为期货 
set_subportfolios([SubPortfolioConfig(cash=init_cash, type='futures')]) 
# 定义一个全局变量, 保存要操作的期货 
g.code1 = 'RB1909.XSGE' 
# 08:30运行自定义开盘前运行函数 
run_daily(before_market_open, time='08:30', reference_security='RB9999.XSGE') 
# 15:30运行自定义收盘后运行函数 
run_daily(after_market_open, time='15:30', reference_security='RB9999.XSGE') 
# 开盘前运行函数 
def before_market_open(context): 
# 订阅要操作的期货 
subscribe(g.code1, 'tick') 
# 有tick事件时运行函数 
def handle_tick(context, tick): 
# 获取最新的 tick 数据 
tick_data = get_current_tick(g.code1) 
print(tick_data) 
# 收盘后运行函数 
def after_market_close(context): 
# 取消今天订阅的标的 
unsubscribe_all()
```

--------------------------------

### Initialize Subportfolios with Equal Cash Distribution

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Sets up three subportfolios, each allocated an equal portion of the initial cash. This is useful for segmenting capital across different asset types or strategies.

```python
init_cash = context.portfolio.starting_cash/3
set_subportfolios([SubPortfolioConfig(cash=init_cash, type='stock'),
SubPortfolioConfig(cash=init_cash, type='index_futures'),
SubPortfolioConfig(cash=init_cash, type='stock_margin')])
```

--------------------------------

### Initialize Trading Strategy and Set Options

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Initializes the trading strategy by setting options like using real prices and defining the stock universe for the strategy. This is typically called once at the beginning of a backtest.

```python
# 导入聚宽函数库 
import jqdata 
# 初始化此策略 
def initialize(context): 
# 开启动态复权模式(真实价格) 
set_option('use_real_price', True) 
# 设置我们要操作的股票池,这里我们选择蓝筹股 
g.stocks = ['000001.XSHE','600000.XSHG','600019.XSHG','600028.XSHG','600030.XSHG','600036.XSHG','600519.XSHG','601398.XSHG','601857.XSHG','601988.XSHG'] 
```

--------------------------------

### Place Order by Shares

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Executes an order to buy or sell a specific number of shares.

```python
order(security, quantity)
```

--------------------------------

### Prepare for Trading Day

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Resets the set of stocks bought today and fetches the previous day's closing prices before the trading day begins.

```python
def before_trading_start(context):
    g.today_bought_stocks = set()
    g.last_df = history(1,'1d','close',g.stocks)
```

--------------------------------

### Enable Performance Profiling

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Enables performance profiling for the strategy. This helps in identifying performance bottlenecks.

```python
enable_profile()
```

--------------------------------

### Place Order by Value

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Executes an order to buy or sell a specific monetary value of a security.

```python
order_value(security, value)
```

--------------------------------

### Practical Strategy with Historical Data Analysis

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

A practical strategy that uses historical price data to make trading decisions. It buys full positions if the previous day's price is 1% above the 5-day average and sells to empty positions if it's below the 5-day average.

```python
# Import JoinQuant function library
import jqdata

# Initialization function, set the stock to operate on, benchmark, etc.
def initialize(context):
    # Define a global variable to store the stock to operate on
    # 000001 (Stock: Ping An Bank)
    g.security = '000001.XSHE'
    # Set CSI 300 as the benchmark
    set_benchmark('000300.XSHG')
    # Enable dynamic ex-rights mode (real price)
    set_option('use_real_price', True)
    # Run function
    run_daily(market_open, time='every_bar')
```

--------------------------------

### Initialize Futures Account

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Configures a single account specifically for financial futures trading, allocating the entire initial cash to it. This is necessary because the default 'stock' type subportfolio does not permit futures trading.

```python
def initialize(context):
    ## 设置单个账户
    # 获取初始资金
    init_cash = context.portfolio.starting_cash
    # 设定账户为金融账户,初始资金为 init_cash 变量代表的数值(如不使用设置多账户,默认只有subportfolios[0]一个账户,Portfolio 指向该账户。)
    set_subportfolios([SubPortfolioConfig(cash=init_cash, type='futures')])
    # 设置运行函数的参考(market_open为自定义函数,需要自己实现;参考标的默认为沪深300,需要根据策略自己设置,下面只是举例)
    run_daily(market_open, time='every_bar', reference_security='CU9999.XSGE')
```

--------------------------------

### Initialize Query Object

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Initialize query objects in `process_initialize` to ensure they are persistent and correctly handled after restarts. Query objects returned by `query()` are not directly serializable.

```python
def process_initialize(context):
    g.__query = query(valuation)
```

--------------------------------

### Execute Buy Order Based on Price Conditions

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

This snippet demonstrates how to execute a buy order for a security if its price has increased within a specific range and meets other criteria, such as the daily buy limit and available cash.

```python
# 得到当前价格 
price = data[security].close 
# 获取这只股票昨天收盘价 
last_close = g.last_df[security][0] 
# 如果上一时间点价格已经涨了9.5%~9.9% 
# 今天的涨停价格区间大于1元,今天没有买入该支股票 
if price/last_close > 1.095 
and price/last_close < 1.099 
and data[security].high_limit - last_close >= 1.0: 
# 得到当前资金余额 
cash = context.portfolio.available_cash 
# 计算今天还需要买入的股票数量 
need_count = g.daily_buy_count - len(g.today_bought_stocks) 
# 把现金分成几份, 
buy_cash = context.portfolio.available_cash / need_count 
# 买入这么多现金的股票 
order_value(security, buy_cash) 
# 放入今日已买股票的集合 
g.today_bought_stocks.add(security) 
# 记录这次买入 
log.info("Buying %s" % (security)) 
# 买够5个之后就不买了 
if len(g.today_bought_stocks) >= g.daily_buy_count: 
break 
```

--------------------------------

### Maximize Sharpe Ratio with Weight Constraints

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Optimizes portfolio weights to maximize Sharpe Ratio, with a constraint that each security's weight does not exceed 10%. This snippet demonstrates setting up the optimizer with a specific target and bounds.

```python
optimized_weight = portfolio_optimizer(date=context.previous_date, 
securities = g.buy_list, 
target = MaxSharpeRatio(rf=0.0,weight_sum_equal=0.5, count=250),#无风险利率为0,最大化夏普比率需要约束组合权重的和为0.5 
constraints = [], 
bounds=[Bound(0, 0.1)], 
default_port_weight_range=[0., 1.0], 
ftol=1e-09, 
return_none_if_fail=True)
# 查看优化结果 
print(optimized_weight)
# 优化失败,给予警告 
if type(optimized_weight) == type(None): 
print('警告:组合优化失败') 
# 按优化结果,执行调仓操作 
else: 
total_value = context.portfolio.total_value # 获取总资产 
for stock in optimized_weight.keys(): 
value = total_value * optimized_weight[stock] # 确定每个标的的权重 
order_target_value(stock, value) # 调整标的至目标权重
```

--------------------------------

### Setting Slippage

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Configure slippage to better simulate real market conditions. Use set_slippage to define the slippage parameters.

```python
set_slippage(slippage=0.001)
```

--------------------------------

### Set Dynamic Real Price Mode

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Enables dynamic price adjustment (real price) mode for backtesting. It is recommended to enable this for more accurate simulations.

```python
use_real_price()
```

--------------------------------

### Margin Security Buy to Cover

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Buys back securities to cover a previous short sale, using margin lending. This closes a short position.

```python
marginsec_close(security, quantity)
```

--------------------------------

### Write Data to File

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Writes backtest or simulation data to a file in the investment research environment. Useful for saving results or logs.

```python
write_file(filename, content)
```

--------------------------------

### Logging Information in Backtesting

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Print logs at different levels (info, debug, warn, error) during backtesting. These logs can be viewed in the backtest output.

```python
log.info('This is an informational message.')
log.debug('This is a debug message.')
log.warn('This is a warning message.')
log.error('This is an error message.')
```

--------------------------------

### Equal-Weight Portfolio Optimization

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Sets equal weights for all securities in the buy list. Use when a simple, balanced allocation is desired.

```python
if g.optimizer == 1:
# 模型1:等权重配置
optimized_weight = pd.Series(data=[1.0/len(g.buy_list)]*len(g.buy_list),
index=g.buy_list)
```

--------------------------------

### Get Dominant Future Contract

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves the symbol of the dominant future contract for a given underlying asset. This is often used for continuous futures trading.

```python
get_dominant_future(security)
```

--------------------------------

### Moving Average Crossover Strategy

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Implements a simple moving average crossover strategy. It buys when the price is 5% above the 5-day moving average and sells when it drops to 5% below.

```python
当价格高于5日均线平均价格1.05时买入,当价格低于5日平均价格0.95时卖出。
```

--------------------------------

### Apply Winsorization (Median)

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Applies Winsorization using the median to cap extreme values. This is an alternative method for outlier treatment.

```python
winsorize_med(data, limits)
```

--------------------------------

### Set Real Price Option

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

It is strongly recommended to use real prices for trades in the simulation environment by calling `set_option('use_real_price', True)`.

```python
set_option('use_real_price', True)
```

--------------------------------

### Recording Data in Backtesting

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Log custom data points during backtesting using the record() function. These can be visualized as charts in the backtest results.

```python
record(my_data=context.portfolio.portfolio_value)
```

--------------------------------

### Handling `initialize` after Code Change

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

The `initialize` function runs only once. To modify global variables after code changes, use the `after_code_changed` function.

```python
def initialize(context):
    g.stock = '000001.XSHE'

def after_code_changed(context):
    g.stock = '000002.XSHE'
```

--------------------------------

### Log Buy and Sell Operations

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Logs information about buying or selling a security. This is useful for tracking trading activity within a strategy.

```python
log.info("Buying %s" % (security))
```

```python
log.info("Selling %s" % (security))
```

--------------------------------

### Set Portfolio Bounds

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Sets bounds (minimum and maximum weights) for individual securities within the portfolio.

```python
set_bounds(bounds_functions)
```

--------------------------------

### Read Data from File

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Reads data from a file within the backtest or simulation environment. Useful for loading configuration or historical data.

```python
read_file(filename)
```

--------------------------------

### Buy Stock Based on Price and VWAP

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Buys a stock if its price exceeds 1.005 times the 3-day VWAP and if there is available cash.

```python
elif price > vwap * 1.005 and cash > 0:
    order(security,100)
    log.info("Buying %s" % (security))
```

--------------------------------

### Handle Tick Data for Strategy

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Processes tick data as it arrives, enabling real-time strategy execution based on the latest market information.

```python
handle_tick(tick_data)
```

--------------------------------

### Setting Trading Costs

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Define transaction costs, including brokerage fees and stamp duty. Use set_order_cost to specify these parameters.

```python
set_order_cost(cost=0.0001, type='stock')
```

--------------------------------

### Margin Security Sell (Short Sell)

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Executes a sell order for a security that is not owned, using margin lending. This is a short selling operation.

```python
marginsec_open(security, quantity)
```

--------------------------------

### Place Order to Target Value

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Adjusts the position of a security to a target monetary value.

```python
order_target_value(security, target_value)
```

--------------------------------

### Set Portfolio Constraints

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Sets constraints for the overall portfolio weights. One or more constraint functions can be applied.

```python
set_constraints(constraint_functions)
```

--------------------------------

### Single Stock Trading Strategy

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

This snippet demonstrates a basic trading strategy for a single stock. It calculates a 5-day moving average and buys or sells the stock based on its current price relative to the average. Ensure 'use_real_price' is set to True for accurate backtesting.

```python
import jqdata

# Initialize function, set the stocks, benchmark, and options
def initialize(context):
    # Define the global variable for the stock to operate on
    # 000001 (Stock: Ping An Bank)
    g.security = '000001.XSHE'
    # Set CSI 300 as the benchmark
    set_benchmark('000300.XSHG')
    # Enable dynamic复权 mode (real price)
    set_option('use_real_price', True)

# Function called at each time unit (daily for daily backtesting, per minute for minute backtesting)
def handle_data(context, data):
    security = g.security
    # Get the closing price of the stock for the last 5 days
    close_data = attribute_history(security, 5, '1d', ['close'])
    # Get the average price over the past five days
    MA5 = close_data['close'].mean()
    # Get the price at the previous time point
    current_price = close_data['close'][-1]
    # Get the current cash balance
    cash = context.portfolio.available_cash

    # If the price at the previous time point is 5% higher than the 5-day average, buy with all available cash
    if (current_price > 1.05*MA5) and (cash>0):
        # Use all cash to buy the stock
        order_value(security, cash)
        # Log the buy order
        log.info("Buying %s" % (security))
    # If the price at the previous time point is lower than the 5-day average, sell to empty the position
    elif current_price < 0.95*MA5 and context.portfolio.positions[security].closeable_amount > 0:
        # Sell all shares, setting the final holding amount for this stock to 0
        order_target(security, 0)
        # Log the sell order
        log.info("Selling %s" % (security))

    # Record the price at the previous time point
    record(stock_price=current_price)
```

--------------------------------

### Place Order to Target Shares

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Adjusts the position of a security to a target number of shares.

```python
order_target(security, target_quantity)
```

--------------------------------

### Risk Parity Portfolio Optimization

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Optimizes portfolio weights based on risk parity, where each asset contributes equally to the total portfolio risk. Includes constraints on stock and ETF total weights. Use for risk-balanced portfolios.

```python
elif g.optimizer == 2:
# 模型2:组合风险平价;股票的总权重限制为0到90%,ETF的总权重限制为0到10%;每只标的权重不超过10%
optimized_weight = portfolio_optimizer(date=context.previous_date,
securities = g.buy_list,
target = RiskParity(count=250, risk_budget=None),# risk_budget 为 None默认为每只股票贡献相等
constraints = [MarketConstraint('stock', low=0.0, high=0.9),
MarketConstraint('etf', low=0.0, high=0.1)],
bounds=[Bound(0, 0.1)],
default_port_weight_range=[0., 1.0],
ftol=1e-09,
return_none_if_fail=True)
```

--------------------------------

### Batch Submit Orders

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Submits multiple orders simultaneously in a batch. This is efficient for executing a basket of trades.

```python
batch_submit_orders(orders)
```

--------------------------------

### Record Data for Plotting

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Records data points that can be plotted on charts during backtesting or live trading. Useful for visualizing strategy performance.

```python
record(name=value)
```

--------------------------------

### Set Margin Security Interest Rate

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Sets the interest rate for margin security lending (short selling).

```python
marginsec_interest_rate(rate)
```

--------------------------------

### Handle Minute-Level Trading Data

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Processes trading data on a minute-by-minute basis, focusing on the last two hours of the trading day and limiting daily buys.

```python
def handle_data(context, data):
    if context.current_dt.hour < 13:
        return
    if len(g.today_bought_stocks) >= g.daily_buy_count:
        return
    for security in (g.stocks - g.today_bought_stocks):
```

--------------------------------

### Query Stock Connect Data

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Retrieves data such as Shanghai-Shenzhen-Hong Kong Stock Connect information using finance.run_query.

```python
finance.run_query(query_id)
```

--------------------------------

### Margin Cash Buy

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Executes a buy order using margin cash. This allows for leveraged purchases.

```python
margincash_open(security, quantity)
```

--------------------------------

### Define Stocks for Trading

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Selects a portion of the SSE 50 index constituents and specific ETF funds to form a trading stock pool. This function is intended to be run before the market opens.

```python
def before_market_open(context):
    print('调仓日期:%s'%context.current_dt.date())
    etf = [
    '159902.XSHE',
    '159903.XSHE',
    '510050.XSHG',
    '510880.XSHG',
    '510440.XSHG',
    ]
    g.buy_list = list(get_index_stocks('000016.XSHG')[-15:]) + etf
```

--------------------------------

### Apply Winsorization (Outlier Removal)

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Applies Winsorization to data to cap extreme values, reducing the impact of outliers.

```python
winsorize(data, limits)
```

--------------------------------

### Scheduled Daily Runs

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Schedule functions to run at specific times daily or every trading bar.

```python
run_daily(func1, time='every_bar')
# 11:00定时运行
run_daily(func2, time='11:00')
# 14:00定时运行
run_daily(func3, time='14:00')
# 14:50定时运行
run_daily(func2, time='14:50')
```

--------------------------------

### Set Futures Margin Rate

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Sets the margin rate for futures trading. This affects the leverage available for futures positions.

```python
futures_margin_rate(security, rate)
```

--------------------------------

### Execute Trades at Market Open

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Sells any stocks currently held that are not in the predefined buy list. This function is executed when the market opens.

```python
def market_open(context):
    sell_list = set(context.portfolio.positions.keys()) - set(g.buy_list)
    for stock in sell_list:
        order_target_value(stock, 0)
```

--------------------------------

### Sell Stock Based on Price and VWAP

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Sells a stock if its price drops below 0.995 times the 3-day VWAP and if the stock is currently held.

```python
if price < vwap * 0.995 and context.portfolio.positions[security].closeable_amount > 0:
    order(security,-100)
    log.info("Selling %s" % (security))
```

--------------------------------

### Execute Trades Based on Halloween Effect Strategy

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Implements a trading logic within the handle_data function to buy stocks based on the 'Halloween Effect' seasonality, specifically buying after October 15th if certain conditions are met.

```python
# 每个单位时间(如果按天回测,则每天调用一次,如果按分钟,则每分钟调用一次)调用一次 
def handle_data(context, data): 
# 得到每只股票可以花费的现金,这里我们使用总现金股票数数量 
cash = context.portfolio.available_cash / len(g.stocks) 
# 获取数据 
hist = history(1,'1d','close',g.stocks) 
# 循环股票池 
for security in g.stocks: 
# 得到当前时间 
today = context.current_dt 
# 得到该股票上一时间点价格 
current_price = hist[security][0] 
# 如果当前为10月且日期大于15号,并且现金大于上一时间点价格,并且当前该股票空仓 
if today.month == 10 and today.day > 15 and cash > current_price and context.portfolio.positions[security].closeable_amount == 0: 
order_value(security, cash) 
```

--------------------------------

### Set Margin Cash Interest Rate

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Sets the interest rate for margin cash borrowing. This affects the cost of leveraged trading.

```python
margincash_interest_rate(rate)
```

--------------------------------

### Sell Logic Based on Date and Holdings

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Executes a sell order if the current date is in May after the 15th and if there is a holding of the specified security. This snippet demonstrates conditional selling.

```python
elif today.month == 5 and today.day > 15 and context.portfolio.positions[security].closeable_amount > 0:
order_target(security, 0)
```

--------------------------------

### Set Margin Security Margin Rate

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Sets the margin rate required for margin security lending (short selling).

```python
marginsec_margin_rate(rate)
```

--------------------------------

### Minimum Variance Portfolio Optimization

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Minimizes the portfolio's variance (risk) while meeting a target annualized return. Use when the primary goal is to reduce volatility.

```python
elif g.optimizer == 3:
# 模型3:组合风险最小化(最小化组合方差);组合总权重限制为90%到100%;组合年化收益率目标下限为10%
optimized_weight = portfolio_optimizer(date=context.previous_date,
securities = g.buy_list,
target = MinVariance(count=250),
constraints = [WeightConstraint(low=0.9, high=1.0),
AnnualProfitConstraint(limit=0.1, count=250)],
bounds=[],
default_port_weight_range=[0., 1.0],
ftol=1e-09,
return_none_if_fail=True)
```

--------------------------------

### Factor Value Maximization Portfolio Optimization

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Maximizes a specific factor's value (e.g., 'popularity indicator 5-day average') within the portfolio, subject to constraints on annualized return and individual security weights. Use when aiming to optimize based on a specific fundamental or technical factor.

```python
elif g.optimizer == 4:
# 模型4:组合标的因子值最大化
# 定义因子:人气指标5日均值
class AR(Factor):
name = 'ar'
# 每天获取过去五日的数据
max_window = 5
# 获取的数据是人气指标
dependencies = ['AR']
def calc(self, data):
return data['AR'].mean()
# 模型4:'人气指标5日均值'最大化;组合年化收益率目标下限为10%;每只标的权重不超过20%
optimized_weight = portfolio_optimizer(date=context.previous_date,
securities = g.buy_list,
target = MaxFactorValue(factor=AR, count=1),
constraints = [AnnualProfitConstraint(limit=0.2, count=250)],
bounds=[Bound(0, 0.2)],
default_port_weight_range=[0., 1.0],
ftol=1e-09,
return_none_if_fail=True)
```

--------------------------------

### Set Margin Cash Margin Rate

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Sets the margin rate required for margin cash borrowing. This determines the leverage available.

```python
margincash_margin_rate(rate)
```

--------------------------------

### Apply Technical Analysis Indicators

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Calculates and applies various technical analysis indicators to securities data.

```python
technical_analysis(securities, date, indicators)
```

--------------------------------

### Subscribe to Tick Events

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Subscribes to receive tick-level data events for specified securities. This is necessary for real-time tick processing.

```python
subscribe(securities, event_type='tick')
```

--------------------------------

### Apply Neutralization

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Applies neutralization techniques to factor data, often used to remove market or industry effects.

```python
neutralize(factor_data, method)
```

--------------------------------

### Margin Security Direct Return

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Directly returns borrowed securities to cover a short position, without buying them on the market.

```python
marginsec_direct_refund(security, quantity)
```

--------------------------------

### Apply Standardization (Z-score)

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Applies standardization to data, converting it to Z-scores. This is useful for comparing variables with different scales.

```python
standardlize(data)
```

--------------------------------

### Query Stock Industry

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Queries the industry to which a specific stock belongs.

```python
get_industry(security)
```

--------------------------------

### Set Optimization Objective Function

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Defines the objective function for portfolio optimization. Only one objective can be selected.

```python
set_objective(objective_function)
```

--------------------------------

### Normalize Stock Code Format

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Converts stock codes to a standardized format. This is useful for ensuring consistency when referencing securities.

```python
normalize_code(stock_code)
```

--------------------------------

### Apply Alpha 101 Factor

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Applies the Alpha 101 factor, a set of quantitative factors designed for alpha generation.

```python
alpha101(securities, date)
```

--------------------------------

### Apply Alpha 191 Factor

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Applies the Alpha 191 factor, another set of quantitative factors for alpha generation.

```python
alpha191(securities, date)
```

--------------------------------

### Query Financial Data

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Queries fundamental financial data for securities. This function is essential for fundamental analysis.

```python
get_fundamentals(securities, start_date, end_date, fields)
```

--------------------------------

### Margin Cash Repay

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Sells securities to repay borrowed margin cash. This is used to reduce leverage or meet margin calls.

```python
margincash_close(security, quantity)
```

--------------------------------

### Account Cash In/Out

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Manages cash movements into and out of the trading account. This is typically used for funding or withdrawals.

```python
inout_cash(account, amount)
```

--------------------------------

### Query Continuous Financial Data

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Queries financial data over multiple consecutive days. This is useful for analyzing trends in financial metrics.

```python
get_fundamentals_continuously(securities, start_date, end_date, fields)
```

--------------------------------

### Batch Cancel Orders

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Cancels multiple pending orders simultaneously in a batch.

```python
batch_cancel_orders(order_ids)
```

--------------------------------

### Send Custom Message

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Sends a custom message, which can be used for alerts or notifications during strategy execution.

```python
send_message(message)
```

--------------------------------

### Margin Cash Direct Refund

Source: https://drive.google.com/file/d/1S23wggXYfdm9wMual2Fq3zN0_pKjOYP5/view

Directly repays borrowed margin cash with available funds, without selling securities.

```python
margincash_direct_refund(amount)
```
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